Systems and Methods for Cluster Augmentation of Search Results

1. A method for cluster augmentation of search results comprising: utilizing at least one processor to execute computer code configured to perform the steps of: clustering a plurality of nodes representing a document belonging to a community within a network via: identifying one or more communities comprising clusters of documents linked together within the network, wherein the one or more communities are identified based on connectivity among nodes within the network; wherein said identifying comprises identifying one or more clusters based on a user definable connectivity factor by performing the steps of: generating a connectivity context for each of the plurality of nodes, the connectivity context including a set of features, wherein a feature is selected from the group consisting of inlinks and outlinks and wherein the set of features comprises an ordered list of features; generating a feature vector for the set of features for each of the plurality of nodes; and ordering the feature vectors; determining one or more key terms from the ordered feature vectors to define the one or more clusters, wherein the one or more key terms comprise a topic associated with the one or more clusters and wherein the determining one or more key terms comprises extracting text from the one or more documents included in the one or more documents and identifying a predetermined number of key terms having a frequency occurrence above a predetermined a frequency by performing a frequency analysis on the extracted text and filtering irrelevant elements from the extracted text using a text-mining technique; and removing hubs and authorities based on the user definable connectivity factor to size the one or more clusters to a predetermined size based on the connectivity of the node remaining after removal of the hubs and authorities; receiving, responsive to the clustering, a query including one or more search terms; and returning one or more results in response to the query, including one or more clusters having one or more key terms corresponding to the one or more search terms.

2. The method according to claim 1 , further comprising selecting one or more advertisements for the one or more clusters, wherein the one or more results include one or more advertisements.

3. The method according to claim 2 , wherein the one or more advertisements are selected according to the one or more key terms for the one or more clusters.

4. The method according to claim 1 , wherein the user definable connectivity factor establishes an amount of type one nodes to be removed from a network graph in determining connectivity among the plurality of nodes, the type one nodes comprising one or more of hubs and authorities.

5. The method according to claim 1 , wherein the network comprises a social network, and further wherein the plurality of nodes comprise a plurality of web pages.

6. The method according to claim 5 , wherein the connectivity among the plurality of nodes is established by one or more hyperlinks between two or more web pages.

7. The method according to claim 1 , wherein determining one or more key terms for the one or more clusters further comprises analyzing text of the plurality of nodes to determine the one or more key terms.

8. The method according to claim 7 , further comprising: associating one or more topics for the one or more clusters based on the one or more key terms; and associating one or more advertisements with the one or more clusters based on the one or more topics.

9. A computer program product for cluster augmentation of search results comprising: a non-transitory computer readable storage medium having computer readable program code embodied therewith, the computer readable program code comprising: computer readable program code configured to cluster a plurality of nodes representing a document belonging to a community within a network via: identifying one or more communities comprising clusters of documents linked together within the network, wherein the one or more communities are identified based on connectivity among nodes within the network; wherein the identifying comprises identifying one or more clusters based on a user definable connectivity factor by performing the steps of: generating a connectivity context for each of the plurality of nodes, the connectivity context including a set of features, wherein a feature is selected from the group consisting of inlinks and outlinks and wherein the set of features comprises an ordered list of features; generating a feature vector for the set of features for each of the plurality of nodes; and ordering the feature vectors; determining one or more key terms from the ordered feature vectors to define the one or more clusters, wherein the one or more key terms comprise a topic associated with the one or more clusters and wherein the determining one or more key terms comprises extracting text from the one or more documents included in the one or more documents and identifying a predetermined number of key terms having a frequency occurrence above a predetermined threshold by performing a frequency analysis on the extracted text and filtering irrelevant elements from the extracted text using a text-mining technique; and removing hubs and authorities based on the user definable connectivity factor to size the one or more clusters to a predetermined size based on the connectivity of the node remaining after removal of the hubs and authorities; computer readable program code configured to receive, responsive to the clustering, a query including one or more search terms; and computer readable program code configured to return one or more results in response to the query, including one or more clusters having one or more key terms corresponding to the one or more search terms.

10. The computer program product according to claim 9 , further comprising computer readable program code configured to select one or more advertisements for the one or more clusters, wherein the one or more results include one or more advertisements.

11. The computer program product according to claim 10 , wherein the one or more advertisements are selected according to the one or more key terms for the one or more clusters.

12. The computer program product according to claim 9 , wherein the user definable connectivity factor establishes an amount of type one nodes to be removed from a network graph in determining connectivity among the plurality of nodes, the type one nodes comprising one or more of hubs and authorities.

13. The computer program product according to claim 9 , wherein the network comprises a social network, and further wherein the plurality of nodes comprise a plurality of web pages.

14. The computer program product according to claim 13 , wherein the connectivity among the plurality of nodes is established by one or more hyperlinks between two or more web pages.

15. The computer program product according to claim 9 , wherein to determine one or more key terms for the one or more clusters further comprises analyzing text of the plurality of nodes to determine the one or more key terms.

16. The computer program product according to claim 15 , further comprising computer readable program code configured to: associate one or more topics for the one or more clusters based on the one or more key terms; and associate one or more advertisements with the one or more clusters based on the one or more topics.

17. A system for cluster augmentation of search results comprising: one or more processors; and a memory operatively connected to the one or more processors; wherein, responsive to execution of computer readable program code accessible to the one or more processors, the one or more processors are configured to: cluster a plurality of nodes representing a document belonging to a community within a network via: identifying one or more communities comprising clusters of documents linked together within the network, wherein the one or more communities are identified based on connectivity among nodes within the network; wherein the identifying comprises identifying one or more clusters based on a user definable connectivity factor by performing the steps of: generating a connectivity context for each of the plurality of nodes, the connectivity context including a set of features, wherein a feature is selected from the group consisting of inlinks and outlinks and wherein the set of features comprises an ordered list of features; generating a feature vector for the set of features for each of the plurality of nodes; and ordering the feature vectors; determining one or more key terms from the ordered feature vectors to define the one or more clusters, wherein the one or more key terms comprise a topic associated with the one or more clusters and wherein the determining one or more key terms comprises extracting text from the one or more documents included in the one or more documents and identifying a predetermined number of key terms having a frequency occurrence above a predetermined threshold by performing a frequency analysis on the extracted text and filtering irrelevant elements from the extracted text using a text-mining technique; and removing hubs and authorities based on the user definable connectivity factor to size the one or more clusters to a predetermined size based on the connectivity of the node remaining after removal of the hubs and authorities; receive, responsive to the clustering, a query including one or more search terms; and return one or more results in response to the query, including one or more clusters having one or more key terms corresponding to the one or more search terms.

18. The system according to claim 17 , wherein the one or more processors are further configured to select one or more advertisements for the one or more clusters, and further wherein the one or more results include one or more advertisements.